Chapter 4 The Value of Chapter 4 The Value of InformationInformation

Chapter 4 The Value of Chapter 4 The Value of InformationInformation

Qi Xu

Professor of Donghua University

Tel: 021-62378860E-mail: xuqi@dhu.edu.cn

Case: Barilla SpA Part ACase: Barilla SpA Part A

Barilla SpA is the world’s largest pasta manufacturer

The company sells to a wide range of Italian retailers, primarily through third party distributors

During the late 1980s, Barilla suffered increasing operational inefficiencies and cost penalties that resulted from large week-to-week variations in its distributors’ order patterns

Barilla dry product factories

Barilla CDCs

Grande Distribution

18 Barilla run depots

Distribution Organization

Chain Supermarkets

Signora Maria shops

Independent Supermarkets

TL=Delivery in truckload quantities

LTL=Delivery in less-than-truckload quantities

65% TL

90%10%

35%

TL

TL

LTL

LTL LTL

Barilla distribution patterns

70% 30%

Weekly Demand for Barilla Dry Products from Northeast DC to CDC

DistributionDistribution

Distributor Ordering Procedures. Most distributors checked their inventory levels and placed orders with Barilla once a week. Barilla products would then be shipped to the distributor over the course of the week that started 8 days after the order was placed and ended 14 days—the average lead time was 10 days.

Most distributors used simple periodic review inventory systems. Nearly all of them had computer-supported ordering systems but few had forecasting systems or sophisticated analytical tools for determining order quantities.

Barilla increasingly felt the effects of fluctuating demand. Orders for Barilla dry products often swung wildly from week to week( see Figure 1).

Distributors’ service level to the retailers were unacceptable.Asking distributors or retailers to carry additional inventory to check the fluctuation.

Distributors and retailers were already carrying too much inventory.

Questions:Questions:

What exactly is causing the distributor’s order pattern to look this way?

What are the underlying drivers of the fluctuations?

Causes of Demand FluctuationsCauses of Demand Fluctuations

Transportation discounts Volume discount Promotional activity No minimum or maximum order quantitie

s Product proliferation(激增 ) Long order lead times Poor customer service rates Poor communication

Demand FluctuationsDemand Fluctuations

The extreme fluctuation in Exhibit 1 is truly remarkable when one considers the underlying aggregate demand for pasta in Italy.

What does the underlying consumer demand pattern for pasta look like in Italy?

What is the impact of demand fluctuationWhat is the impact of demand fluctuationseen in Exhibit 1?seen in Exhibit 1?

Because the plant has high product change over costs, Barilla has either inefficient production or excess finished goods inventory

Utilization of central distribution is low Workers Equipment

Transportation costs are higher than necessary

What is the impact of demand fluctuationWhat is the impact of demand fluctuation

The distributor must build excess capacity to hold goods bought on any type of promotion, including quantity discounts, truckload discounts and canvass period discounts What if the distributor passes the discount along

to the retailers? What is the value of the promotion game?

Barilla SpA Part A (continued)Barilla SpA Part A (continued)

To address this problem, the director of logistics suggests: Implementation of Just-in-Time Distribution

(JITD) with Barilla’s distributors. What is the JITD System?

Decision-making authority for determining shipments from Barilla to a distributor would transfer from the distributor to Barilla

Rather than simply filling orders specified by the distributor, Barilla would monitor the flow of its product through the distributor’s warehouse, and then decide what to ship to the distributor and when to ship it.

Evaluation of the JITD ProposalEvaluation of the JITD Proposal

Clearly the variation in demand is imposing additional costs on the channel. What do you think of the JITD proposal as a mechanism for reducing these costs?

Why should this work? How does it work? What makes Barilla think that it can do a better

job of determining a good product/delivery sequence than its distributors?

Two Key Concepts Behind JITDTwo Key Concepts Behind JITD

Replace sequential optimization with global optimization Who will optimize?

Eliminate some of the ‘false’ economics that drive traditional ordering processes What does this mean?

Implementation IssuesImplementation Issues Resistance from the Distributors Resistance from the Distributors

“Managing stock is my job; I don’t need you to see my warehouse or my figures.”

“I could improve my inventory and service level myself if you would deliver my orders more quickly; I would place my order and you would deliver within 36 hours.”

“We would be giving Barilla the power to push products into our warehouse just so that Barilla can reduce its costs.”

?

Implementation Issues Implementation Issues Resistance from Sales and MarketingResistance from Sales and Marketing

“Our sales levels would flatten if we put this program in place.”

“How can we get the trade to push Barilla product to retailers if we don’t offer some sort of incentive?”

“If space is freed up in our distributors’ warehouses…the distributors would then push our competitors’ product more than ours.”

“…the distribution organization is not yet ready to handle such a sophisticated relationship.”

?

How Can BarillaHow Can BarillaSolve the Implementation Problems?Solve the Implementation Problems?

Demonstrate that JITD benefits the distributors (lowering inventory, improving their service levels and increasing their returns on assets); Run experiment at one or more of Barilla’s 18 depots

Barilla needs to look at JITD not as a logistics program, but as a company-wide effort; Get top management closely involved

Trust

Barilla (B) CaseBarilla (B) Case

What did Barilla learn from the experiments in Florence and Milan?

How should Barilla change the way it attempts to sell the JITD concept to its distributors?

If you were a Barilla distributor, would you sign onto the program after seeing these results?

EvaluationEvaluation

How do you evaluate the implementation process Barilla used with Cortese?

Two important issues:Two important issues:

Variations in distributors’ order patterns have caused severe operational inefficiencies and cost penalties for Barilla. Indeed, while variability in aggregate demand for pasta is quite small, orders placed by the distributors have a huge variability.

Unlike traditional SC in which distributors place orders and manufacturers try to satisfy these orders as much as possible, in JITD Barilla would specify the appropriate delivery quantities—those that would more effectively meet the end consumer’s needs yet would also

more evenly distribute the workload on Barilla’s manufacturing and logistics systems.

Such a strategy has been referred to as vendor managed inventory(VMI).

QuestionsQuestions

What are the reasons for the increase in variability in Barilla’s SC?

How can the firm cope with the increase in variability?

What is the impact of transferring demand information across the SC?

Can the VMI strategy solve the operational problems faced by Barilla?

Chapter 5 Value of InformationChapter 5 Value of Information “In modern supply chains, information replaces

inventory” Why is this true? Why is this false?

Information is always better than no information. Why?

Information Helps reduce variability Helps improve forecasts Enables coordination of systems and strategies Improves customer service Facilitates lead time reductions Enables firms to react more quickly to changing

market conditions.

The Bullwhip EffectThe Bullwhip Effect and its Impact on the Supply Chain and its Impact on the Supply Chain

Consider the order pattern of a color television model sold by a large electronics manufacturer to one of its accounts, a national retailer.

Fig 1. Order Stream

Fig 2. Point-of-sales Data-Original

Figure 3. POS Data After Removing

Promotions

The Bullwhip EffectThe Bullwhip Effect and its Impact on the Supply Chain and its Impact on the Supply Chain

Figure 4. POS Data After Removing Promotion & Trend

The Bullwhip EffectThe Bullwhip Effectand its Impact on the Supply Chainand its Impact on the Supply Chain

Higher Variability in Orders Placed by Higher Variability in Orders Placed by Computer Retailer to Manufacturer Than Computer Retailer to Manufacturer Than Actual SalesActual Sales

Increasing Variability of Orders Increasing Variability of Orders Up the Supply ChainUp the Supply Chain

Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review

We Conclude ….We Conclude ….

Order variability is amplified up the supply chain; upstream echelons face higher variability.

Consequences….Consequences….

Increased safety stockReduced service levelInefficient allocation of

resourcesIncreased transportation

costs

What are the Causes….What are the Causes….

Promotional sales Forward buying

Volume and transportation discounts Batching

Inflated orders IBM Aptiva orders increased by 2-3 times when

retailers thought that IBM would be out of stock over Christmas

What are the Causes….What are the Causes….

Demand forecasting Order-up-to points are modified as forecasts change

– orders increase more than forecasts Long cycle times

Long lead times magnify this effect

Consider a simple supply chain…Consider a simple supply chain…

Single retailer, single manufacturer. Retailer observes customer demand, Dt. Retailer orders qt from manufacturer.

Retailer ManufacturerDt

qt

L

Consider a simple supply chain…Consider a simple supply chain…

Average annual demand is 5,200 units, and shipments are made to the store each week. If the variability in orders placed is low, so that the shipment every week is capacity need by only about 100 units.

If weekly variability is very high, so that during certain weeks makers must make and ship 400 units and weeks no units at all, it is easy to see that production and shipping capacity must be much higher and that some weeks is capacity will be idle.

Quantifying the Bullwhip EffectQuantifying the Bullwhip Effect

Suppose a P period moving average is used.

(移动平均法 ) If the variance of the customer demand seen by th

e retailer is Var(D), then the variance of the orders placed by that retailer to the manufacturer, Var(Q), relative to the variance of customer demand satisfies:

2

2221

)(

)(

P

L

P

L

DVar

qVar

零售商向制造商发出的订单需求的方差 Var(Q) 与顾客需求的方差之比

Var(q)/Var(D):Var(q)/Var(D):For Various Lead TimesFor Various Lead Times

L=5

L=3

L=1

0

2

4

6

8

10

12

14

0 5 10 15 20 25 30

L=5

L=3

L=1

P

A lower bound on the increase in variability given as a function of p

Figure shows the lower bound (下限 )on the increase in variability as a function of p for various values of the lead time,L. When p is large, and L is small, the bullwhip effect due to forecasting error is negligible.

The bullwhip effect is magnified as we increase the lead time and decrease p.

Assume p=5, L=1

The variance of the orders placed by the retailer to the manufacturer will be at least 40 percent larger than the variance of the customer demand.

2

2

( ) 2 21 1.4

( )

Var q L L

Var D P P

Multi stage SC systemsMulti stage SC systems

External Demand

Order lead time Delivery lead time

Order lead time Delivery lead time

Order lead time Delivery lead time Production lead time

Retailer

Wholesaler

Distributor

Factory

Multi-Stage Supply ChainsMulti-Stage Supply Chains

Consider a multi-stage supply chain: Stage i places order qi to stage i+1. Li is lead time between stage i and i+1.

RetailerStage 1

Manufacturer Stage 2

Supplier Stage 3

qo=D q1q2

L1 L2

SC with centralized Demand Information

Centralized: each stage bases orders on retailer’s forecast demand.

The retailer observes customer demand, forecasts the mean demand using a moving average with p demand observations, finds his target inventory level based on the forecast mean demand, and places an order to the wholesaler.

The wholesaler receives order along with the retailer’s forecast mean demand, uses this forecast to determine his target inventory level, and place an order to the distributor.

Similarly, the distributor places order to the factory.

SC with centralized Demand Information (cont’)

In this centralized SC, each stage of the SC receives the retailer’s forecast mean demand and follows and order-up-to inventory policy based on this mean demand.

2

2

11

221

)(

)(

P

L

P

L

DVar

qVar

k

ii

k

iik

The variance of the orders placed by the kth stage of the SC, Var(Qk), relative to the variance of the customer demand, Var(D), is just:

供应链上第 k阶段发出订单的方差 Var(qk) 相对于顾客需求的方差 Var(D) 的

关系 :

SC with centralized Demand Information (cont’)

For example, if the lead time from the retailer to the wholesaler is two periods, then L1=2. Similarly, if the lead time from the wholesaler to the distributor is two periods, then L2=2, and if the lead time from the distributor to the factory is also two periods, then L3=2.

The total lead time from the retailer to the factory is L1+L2+L3=6

This expression for the variance of the orders placed by the kth stage is very similar to the expression in the previous section, with the single stage lead time.

Decentralized Demand informationDecentralized Demand information

Decentralized: each stage bases orders on previous stage’s demand. The retailer does not make its forecast mean demand available to the remainder of

the SC. Instead, the wholesaler must estimate the mean demand based on the orders received from the retailer.

The variance of the orders placed by the kth stage of the SC, Var(Qk),relative to the variance of the customer demand, Var(D) satisfies:

k

i

iik

P

L

P

L

DVar

qVar

12

2221

)(

)(

The variance increases multiplicatively at each stage of the SC.

Multi-Stage Multi-Stage Systems:Var(qSystems:Var(qkk)/Var(D))/Var(D)

0

5

10

15

20

25

30

0 5 10 15 20 25

Dec, k=5

Cen, k=5

Dec, k=3

Cen, k=3

k=1

Increase in variability for centralized and decentralized system

It is now clear that by sharing demand information with each stage of the SC, we can significantly reduce the bullwhip effect.

When demand information is centralized, each stage of the SC can use the actual customer demand data to estimate the average demand.

When demand information is not shared, each stage must use the orders placed by the previous stage to estimate the average demand. These orders are more variable than the actual customer demand data, thus, the forecasts created using these orders are more variable, leading to more variable orders.

The Bullwhip Effect:The Bullwhip Effect:Managerial InsightsManagerial Insights

Exists, in part, due to the retailer’s need to estimate the mean and variance of demand.

The increase in variability is an increasing function of the lead time.

Centralized demand information can significantly reduce the bullwhip effect, but will not eliminate it.

Coping with the Bullwhip Effect Coping with the Bullwhip Effect in Leading Companiesin Leading Companies

Reduce uncertainty POS Sharing information Sharing forecasts and policies

Reduce variability Eliminate promotions Year-round low pricing

Reduce lead times EDI Cross docking

Strategic partnerships Vendor managed inventory (VMI) Data sharing

Example:Example: Quick Response at Benetton Quick Response at Benetton

Benetton, the Italian sportswear manufacturer, was founded in 1964. In 1975 Benetton had 200 stores across Italy.

Ten years later, the company expanded to the U.S., Japan and Eastern Europe. Sales in 1991 reached 2 trillion.

Many attribute Benetton’s success to successful use of communication and information technologies.

Example:Example:Quick Response at BenettonQuick Response at Benetton

Benetton uses an effective strategy, referred to as Quick Response, in which manufacturing, warehousing, sales and retailers are linked together. In this strategy a Benetton retailer reorders a product through a direct link with Benetton’s mainframe computer in Italy.

Using this strategy, Benetton is capable of shipping a new order in only 4 weeks, several week earlier than most of its competitors.

How Does BenettonHow Does BenettonCope with the Bullwhip Effect?Cope with the Bullwhip Effect?

1. Integrated Information Systems

• Global EDI network that links agents with production and inventory information

• EDI order transmission to HQ

• EDI linkage with air carriers

• Data linked to manufacturing

2. Coordinated Planning

• Frequent review allows fast reaction

• Integrated distribution strategy

Information for Effective Information for Effective ForecastsForecasts

Pricing, promotion, new products Different parties have this information Retailers may set pricing or promotion without

telling distributor Distributor/Manufacturer might have new

product or availability information Collaborative Forecasting addresses

these issues.

Information for Coordination of Information for Coordination of SystemsSystems

Information is required to move from local to global optimization

Questions: Who will optimize? How will savings be split?

Information is needed : Production status and costs Transportation availability and costs Inventory information Capacity information Demand information

Locating Desired ProductsLocating Desired Products

How can demand be met if products are not in inventory? Locating products at other stores What about at other dealers?

What level of customer service will be perceived?

Lead-Time ReductionLead-Time Reduction

Why? Customer orders are filled quickly Bullwhip effect is reduced Forecasts are more accurate Inventory levels are reduced

How? EDI POS data leading to anticipating incoming orders.

Information to Address ConflictsInformation to Address Conflicts

Lot Size – Inventory: Advanced manufacturing systems POS data for advance warnings

Inventory -- Transportation: Lead time reduction for batching Information systems for combining shipments Cross docking Advanced DSS

Lead Time – Transportation: Lower transportation costs Improved forecasting Lower order lead times

Product Variety – Inventory: Delayed differentiation

Cost – Customer Service: Transshipment

Supply

Sources:plantsvendorsports

RegionalWarehouses:stocking points

Field Warehouses:stockingpoints

Customers,demandcenterssinks

Production/purchase costs

Inventory &warehousing costs

Transportation costs Inventory &

warehousing costs

Transportation costs